The present disclosure relates generally to asset reliability forecasting and, in particular, to asset reliability forecasting and event-based asset selection.
Selecting the most appropriate assets for use in an operation requires some degree of knowledge of the individual units of a particular group of assets, such as age, service/maintenance history, etc. For example, given a fleet of commercial trucks it may be useful to know each vehicle's repair history before determining which vehicle should be assigned to a particular operation. In this example, a newer truck that has had minimal repair issues may be chosen for a cross-country operation, while an older truck with a less-than-optimal repair record may be selected for an intra-state operation. Other information that may be useful in the selection process includes logistical information (e.g., the current location of a vehicle with respect to the starting point and destination of the operation), the sensitivity of the operation (e.g., valuable, fragile, or perishable cargo), environmental considerations (e.g., extreme heat, rough terrain), and time-sensitive considerations, to name a few.
For large groups of assets, these determinations can become complex and fraught with error. Moreover, for particular types of assets and operations, the various information elements used in making these determinations oftentimes change dynamically over time, making the asset selection process even more uncertain.
What is needed, therefore, is a way to identify and select the most appropriate assets for operations or events and to predict future performance of the assets based upon changing criteria over time.